Rule-Based Learner Competencies Predictor System

Forecasting the academic achievement of students is a critical area of research in educational contexts. This domain's significance stems from its ability to develop efficient mechanisms that enhance academic outcomes and minimize student attrition. In this context, rubric-based progressive learning meticulously provides valuable insights into students’ preferences, knowledge, and competencies. This study proposes a recommender model for detecting the Computational Thinking (CT) competencies of programming learners using a rubric and machine learning. A programming rubric was prepared to cover key programming concepts. A quiz conducted afterward was scored as per the rubric designed. Hierarchical clustering was applied to the rubric scores of learners to segment them into four categories according to their learning parameters. The rules were generated as CT competencies using a rule-based classifier—a multiple-layer perceptron neural network, considering cluster categories as labels. The proposed model assists learners and instructors in identifying the learners’ learning capabilities and priorities, resulting in improved learner performances.